Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
A New Model Predictive Torque Control Strategy with Reduced Set of Prediction Vectors
Date
2018-04-12
Author
Şahin, İlker
Keysan, Ozan
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
176
views
0
downloads
Cite This
Major drawback of finite control set model predictive control (FCS-MPC) is its high computational burden. This paper proposes a new optimal vector selection strategy that reduces the computational cost of FCS-MPC technique. Considering two-level voltage source inverters (2L-VSI) utilized as motor drives, proposed strategy reduces the number of active prediction vectors from six to three. Hence, cost function is evaluated only for four vectors (three active and one zero). Moreover, between the two possible zero vector configurations, the one which avoids switching of the maximum current carrying phase arm is selected. Proposed control strategy has been validated by detailed MAT LAB/Simulink models. Required computation time for the control algorithm has been reduced by 30% The dynamic performance of the drive is not degraded with the reduction of active prediction vectors. Compared to the classical FCS-MPC, proposed algorithm offers up to 28% switching loss reduction (9.9% in average) especially in the high torque - low speed region. Simulation models have been made available as open access.
Subject Keywords
Model predictive control
,
Predictive torque control
,
Finite control set
,
Reduced set
,
Computational cost
,
Voltage source inverter
,
Motor drive
URI
https://hdl.handle.net/11511/55760
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
A Control System Architecture for Control of Non-Affine in Control, Open-Loop Unstable Underactuated Systems
Marangoz, Alp; Kutay, Ali Türker (2017-07-25)
In this paper, a control system architecture for control of non-affine in control, open-loop unstable underactuated system is discussed. Passivization of the unactuated (internal) system dynamics achieved through perturbation of trajectories of the actuated states, which are calculated through adaptive dynamic inversion technique, based on Tikhonov's theorem. Performance of the controller is shown through simulation of two open-loop unstable and locally uncontrollable example problems.
A simplified discrete-time implementation of FCS-MPC applied to an IM drive
ŞAHİN, İLKER; Keysan, Ozan (2019-01-01)
Model predictive control (MPC) has drawn significant attention from the power electronics research community in the last decade. Regarding the application of MPC in motor control, several studies have been conducted that include design and implementation of various predictive torque control techniques. In this study, MPC of an induction motor is implemented via TMDXIDDK379D, a motor drive development platform produced by Texas Instruments (TI). The main motivation is to show the engineers and researchers a ...
An artificial neural network estimator design for the inferential model predictive control of an industrial multi-component distillation column
Bahar, Almila; Özgen, Canan; Department of Chemical Engineering (2003)
An inferential control methodology, that utilizes an artificial neural network (ANN) estimator for a model predictive controller, is developed for an industrial multi-component distillation column. In the column, propane and butane is separated from a mixture of propane, n-butane, i-butane, and i-pentane with a top product purity of 96% propane and a bottom product purity of 63% n- butane. Dual composition control of the column must be used in a multivariable model predictive controller for an efficient ope...
A novel modal superposition method with response dependent nonlinear modes for periodic vibration analysis of large MDOF nonlinear systems
Ferhatoglu, Erhan; Ciğeroğlu, Ender; Özgüven, Hasan Nevzat (Elsevier BV, 2020-01-01)
Design of complex mechanical structures requires to predict nonlinearities that affect the dynamic behavior considerably. However, finding the forced response of nonlinear structures is computationally expensive, especially for large ordered realistic finite element models. In this paper, a novel approach is proposed to reduce computational time significantly utilizing Response Dependent Nonlinear Mode (RDNM) concept in determining the steady state periodic response of nonlinear structures. The method is ap...
An improved method for inference of piecewise linear systems by detecting jumps using derivative estimation
Selcuk, A. M.; Öktem, Hüseyin Avni (Elsevier BV, 2009-08-01)
Inference of dynamical systems using piecewise linear models is a promising active research area. Most of the investigations in this field have been stimulated by the research in functional genomics. In this article we study the inference problem in piecewise linear systems. We propose first identifying the state transitions by detecting the jumps of the derivative estimates, then finding the guard conditions of the state transitions (thresholds) from the values of the state variables at the state transitio...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
İ. Şahin and O. Keysan, “A New Model Predictive Torque Control Strategy with Reduced Set of Prediction Vectors,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55760.